To probe the statistical implication of the planet candidate signals, a method of stacking the Bayesian General Lomb-Scargle (BGLS) periodograms has been used. This tool was developed for identifying the periodicities caused by stellar activity, and to show how it can be used to track the signal-to-noise ratio (SNR) of the detection over time [1]}. Adding in more observations, should increase the power or SNR measure of the signal, if the signal is real and coherent. On the contrary, if the signal is incoherent or short-lived, adding more data would cause the SNR or Lomb-Scargle power to decrease [2]}, [3]}, [1]}. Hence, signals arising from stellar activity can be identified by monitoring their variable and incoherent nature. To track the significance of periodic signals, the BGLS periodogram was calculated for a smaller data subset and recalculated repeatedly with subsequent addition of observations.
